, Volume 55, Issue 6, pp 1633–1640 | Cite as

Microvascular autoregulation in children and adolescents with type 1 diabetes mellitus

  • O. Schlager
  • A. Hammer
  • A. Willfort-Ehringer
  • M. Fritsch
  • B. Rami-Merhar
  • E. Schober
  • K. Nagl
  • A. Giurgea
  • C. Margeta
  • S. Zehetmayer
  • G. H. Schernthaner
  • R. Koppensteiner
  • M. E. GschwandtnerEmail author



Deterioration of microvascular function may have an early onset in individuals with type 1 diabetes mellitus. We hypothesised that microvascular autoregulation is impaired in children with type 1 diabetes and can be detected non-invasively by postocclusive reactive hyperaemia (PORH).


Microvascular autoregulation was assessed in 58 children with type 1 diabetes and 58 age- and sex-matched healthy controls by PORH using laser Doppler fluxmetry. Baseline perfusion, biological zero (defined as a ‘no flow’ laser Doppler signal during suprasystolic occlusion), peak perfusion following occlusion, time to peak and recovery time (time until baseline perfusion is resumed) were recorded and compared between the groups.


Peak perfusion was higher in children with type 1 diabetes than in healthy controls (1.7 ± 0.93 AU [arbitrary units] vs 1.29 ± 0.46 AU; p = 0.004), and biological zero was lower in children with type 1 diabetes vs controls (0.14 ± 0.04 AU vs 0.19 ± 0.04 AU; p < 0.0001). No differences were seen between the groups in baseline perfusion, time to peak during PORH and recovery time following PORH.


PORH reveals impaired microvascular autoregulation in children with type 1 diabetes. The higher peak perfusion might reflect a decline in the vasoconstrictive ability of arteriolar smooth muscle cells upstream of capillary beds in children with type 1 diabetes.


Autoregulation Children Laser Doppler Microcirculation Type 1 diabetes mellitus 



Arbitrary units


Standard deviation score


Postocclusive reactive hyperaemia


Type 1 diabetes mellitus is a major risk factor for the development of atherosclerosis. Epidemiological data on type 1 diabetes have shown an age- and sex-standardised annual incidence rate of 8–15 per 100,000 person-years among children (age below 15 years) in central Europe [1, 2]. In recent years, the incidence of type 1 diabetes in childhood has even increased. Type 1 diabetes in childhood might promote the atherosclerotic process at an early stage [3]. Apart from macrovascular complications, such as ischaemic heart disease, microvascular injury is a matter of concern in patients with type 1 diabetes [4, 5].

Microcirculatory autoregulation can be assessed non-invasively by laser Doppler fluxmetry measuring postocclusive reactive hyperaemia (PORH) [6, 7, 8, 9]. Laser Doppler fluxmetry is a non-invasive technique which allows investigation of the microcirculatory network that is situated 1–1.5 mm below the epidermis [10, 11]. Perfusion of this microvascular network of the papillary dermis is mainly regulated by the tone of smooth muscle cells in the vessel wall of afferent arterioles [10].

Previously it has been proposed that, during PORH, vasoconstriction of these smooth muscle cells is provoked by myogenic, neurogenic, metabolic and physical factors [12, 13]. Thereby, precapillary vasoconstriction prevents the terminal microvascular bed from hyperperfusion and subsequently attenuates postocclusive peak perfusion in the microcirculation.

Previous studies have demonstrated that microvascular autoregulation seems to be impaired by metabolic disorders [12, 14]. Importantly, it has been shown that microvascular regulatory dysfunction—when detected by a change in peak perfusion patterns during PORH—is related to an elevated albumin excretion rate as a surrogate marker for end-organ damage and cardiovascular mortality [13, 15]. This may suggest that microcirculatory autoregulation of the skin additionally reflects microvascular function of other vascular beds.

Therefore, we hypothesised that microcirculatory autoregulation of the skin is affected by type 1 diabetes at an early stage and that impairments can easily be detected in children. The aim of the present study was to assess PORH using laser Doppler fluxmetry in children with type 1 diabetes in comparison with age- and sex-matched healthy controls to detect early signs of microvascular dysfunction in type 1 diabetes.


Study design

The study was performed according to the recommendations of the Declaration of Helsinki and the protocol was approved by the local ethics committee (EK 607/2006). Informed assent was obtained from all children and written informed consent was obtained from their parents.

Consecutive children with type 1 diabetes who were admitted to the Department of Pediatrics and Adolescent Medicine at the Medical University of Vienna were eligible for the present study. After inclusion, laboratory tests were obtained at the paediatric outpatient clinic and microvascular function was assessed at the microcirculation laboratory. Type 1 diabetes was diagnosed according to the current recommendations of the American Diabetes Association [16]. Included children with type 1 diabetes were free of clinical manifestations of microvascular damage, i.e. retinal lesions, microalbuminuria and neuropathy were not present in those children.

Healthy children, recruited from Austrian primary and secondary schools within the context of an epidemiological project, served as controls. None had a history of any cardiovascular, metabolic or other chronic disease, and routine laboratory values (including blood glucose and lipids) were within the normal range. BMI was normal in controls according to age- and sex-matched percentiles [17].

Laboratory and clinical data

Fasting venous blood samples were drawn from all children with type 1 diabetes. Serum lipids (total cholesterol, triacylglycerol, LDL-cholesterol and HDL-cholesterol) and blood glucose levels were assessed at the institutional laboratory. HbAlc was measured as a percentage of total haemoglobin using high-performance liquid chromatography at the institutional laboratory.

In healthy controls, lipid levels and fasting blood glucose levels were assessed from whole blood obtained by a finger prick using the Cholestech LDX System (Cholestech Corporation, Hayward, CA, USA). This device uses enzymatic solid-phase technology to measure total cholesterol, HDL-cholesterol, triacylglycerol and glucose levels. All blood tests were conducted by the same trained investigator. According to the manufacturer, the intra-day coefficients of variations range between 2.4% and 2.5% for total cholesterol, 3.4% and 4.8% for HDL-cholesterol, 1.6% and 3.6% for triacylglycerol, 3.8% and 4.9% for LDL-cholesterol, and 4.5% and 6.2% for blood glucose. According to the Cholesterol Reference Method Laboratory Network, the Cholestech LDX System meets the National Cholesterol Education Program criteria for accuracy and precision and is comparable to centralised laboratory testing.

To determine the comparability of the Cholestech LDX System with our institutional laboratory we measured total cholesterol, HDL-cholesterol, triacylglycerol and glucose levels in 15 children using both methods on the same day and observed the following percentages of measurement error for the Cholestech LDX System: 6.3% for total cholesterol, 7% for LDL-cholesterol, 4.9% for HDL-cholesterol, 4.9% for triacylglycerol and 5.7% for blood glucose. Comparing both methods we observed the following correlation coefficients: r = 0.97 for total cholesterol, r = 0.99 for LDL-cholesterol, r = 0.98 for HDL-cholesterol, r = 0.97 for triacylglycerol and r = 0.99 for blood glucose (all p < 0.001).

In all participating children blood pressure was measured on both arms with appropriate cuffs following a resting period of 20 min in a supine position. To account for the influence of sex, age and height on blood pressure in children we calculated blood pressure z scores for systolic and diastolic blood pressure.

Laser Doppler

A laser Doppler (PIM II with LISCA Opto-Isolation Unit; Perimed, Järfälla, Sweden) was used for continuous fluxmetry to assess total skin perfusion at a predefined single measurement site over time. The first interspace between the thumb and the index finger of the dorsum of the patient´s hand was chosen [14, 18]. The skin of the selected site had to be intact and superficial veins were avoided. Using the duplex mode a laser beam (wavelength 632 nm, power 1 mW, beam diameter 1 mm) was directed to the skin at the selected site [6]. The processor bandwidth was 20–13 kHz with a respective time constant of 0.3–0.4 s (laser classification: class 2 according to EN60825-1, and class II according CFR1040.10 and CFR1040.11); the sampling frequency was 1–100 Hz. Sampling frequency and bandwidth were not changed throughout the course of the study.

Through transmission of superficial tissue layers the laser beam is Doppler-shifted by interfering with moving blood cells. The reflected signal derives from a depth 1–1.5 mm below the epidermis and is processed to form a photocurrent [10]. The photocurrent scales linearly with tissue perfusion, which is defined as the product of average speed and concentration of blood cells. The output signal can be displayed as a continuous time–perfusion diagram illustrating the change of microcirculatory blood flow at the selected site.

All children were investigated under standardised conditions in a quiet and darkened room at a constant room temperature of 23.3 ± 0.6°C. All measurements were performed in the morning. Study participants were requested to refrain from coffee or tea intake ahead of laser Doppler measurements. All study participants were non-smokers. Before measurements the children were kept in a supine position for 20 min and a sphygmomanometer cuff was placed around the upper arm above the antecubital area for determination of PORH. All PORH tests were supervised by the same two experienced investigators (O. Schlanger and M.E. Gschwandtner).


Microvascular function was investigated by PORH to determine the following variables: baseline perfusion, biological zero, time to peak, peak perfusion and recovery time.

Baseline perfusion was recorded under resting conditions without proximal arterial occlusion. Biological zero, defined as a ‘no flow’ laser Doppler signal, was measured during proximal arterial occlusion. Arterial occlusion was obtained by suprasystolic cuff inflation (10 mmHg above systolic blood pressure). Referring to previously published recommendations, an occlusion time of 3–5 min has been proposed [19]. In the present study we chose an occlusion time of 3 min, since—especially in children—longer occlusion periods might have been associated with substantial discomfort and a subsequent decline in compliance.

Both baseline perfusion and biological zero were determined as mean perfusion values over the predefined time period (3 min) and were given in arbitrary units (AU). Directly following suprasystolic arterial occlusion, cuff deflation led to immediate hyperaemia, which then provoked vasoconstriction of the smooth muscle cells of the precapillary arterioles.

Peak perfusion (AU) and time to peak (s), defined as the time span between cuff deflation and peak perfusion, were determined. Recovery time (s), the time until mean baseline perfusion was attained for the first time, was measured (Fig. 1).
Fig. 1

Time–perfusion graph illustrating total skin perfusion during PORH when measured by laser Doppler fluxmetry

All perfusion values as well as the measured time spans were determined using device-related software (LDPIWin 2 for Windows,

For determination of reproducibility, we investigated 10 healthy children on 3 consecutive days and found the following intra-individual coefficients of variation: 12.9% for baseline perfusion, 11.4% for biological zero, 16% for time to peak, 19.8% for peak perfusion and 18.3% for recovery time. These coefficients of variation are in accordance with those found in previous investigations [20].

Sample size

In previous investigations using laser Doppler fluxmetry to measure PORH in microcirculation we observed a mean peak perfusion of 1.26 AU with an SD of 0.5 AU for healthy children [14]. According to these data we assumed that diabetic children had a mean peak perfusion of 1.6 AU and an equal SD of 0.5 AU. A sample size of 45 children per group was calculated to have 80% power for detection of the proposed difference using a two-group t test with a 0.05 two-sided significance level. If the SD increased to 0.6 AU, more than 50 children per group would have been needed. Thus, we set the group-wise sample size to 58.

Statistical analysis

Absolute and relative frequencies are given for categorical variables; number of observations, mean and SD, are given for continuous variables in the text. Logistic regression analyses of the target variable, type 1 diabetes, were calculated for the variables baseline perfusion, biological zero, time to peak, peak perfusion and recovery time adjusting for age and sex. The probability of a case was modelled and p values are reported. Finally, we performed a multiple analysis for each significant microcirculation parameter adjusting for all univariately significant risk factors (BMI, total cholesterol, HDL-cholesterol, blood glucose) as well as the matching variables age and sex. Correlations of influencing variables were calculated using Pearson’s method.

Analyses were performed using SAS 9.1 ( and R-2.8 software ( All p values <0.05 were considered to indicate statistical significance.


The cohort comprised 58 children with type 1 diabetes (27 males, 31 females; mean age ± SD: 14.1 ± 1.7 years) and 58 age- and sex-matched healthy children (34 males, 24 females; 13.6 ± 2.0 years). Demographic and clinical data are shown in Table 1. Fasting blood glucose (mean ± SD) was higher in children with type 1 diabetes than in healthy controls (8.48 ± 4.7 vs 4.74 ± 0.39 mmol/l). In children with type 1 diabetes the mean (± SD) duration of disease was 7.8 ± 3.3 years and the mean HbAlc was 7.9 ± 1.0% (63.4 ± 11.4 mmol/mol). Body weight, BMI and the corresponding BMI standard deviation score (BMI-SDS) were higher in children with type 1 diabetes than in healthy controls. Further, total cholesterol and HDL-cholesterol levels were higher in the type 1 diabetes group. Absolute diastolic blood pressure was higher in healthy controls than in children with type 1 diabetes, while blood pressure z scores did not vary significantly between groups.
Table 1

Demographic data and clinical characteristics of children with type 1 diabetes and healthy controls


Type 1 diabetes


p value


(n = 58)

(n = 58)


Age (years)

14.1 ± 1.7

13.6 ± 2.0


Male (n)

27 (46.6%)

34 (58.6%)


Body weight (kg)

59.5 ± 11.6

48.6 ± 11.1


Body height (cm)

163.3 ± 10.1

159.3 ± 10.3


BMI (kg/m2)

22.2 ± 3.1

18.9 ± 2.5



0.79 ± 0.74

−0.16 ± 0.8


Systolic blood pressure (mmHg)

117.2 ± 11.1

114.5 ± 10.1


Systolic blood pressure z score

0.52 ± 1.01

0.22 ± 0.9


Diastolic blood pressure (mmHg)

61.7 ± 6.9

72.3 ± 10.0


Diastolic blood pressure z score

0.07 ± 0.59

0.18 ± 0.83


Fasting blood glucose (mmol/l)

8.48 ± 4.7

4.74 ± 0.39


Total cholesterol (mmol/l)

4.48 ± 0.83

3.96 ± 0.65


Triacylglycerol (mmol/l)

1.25 ± 0.69

1.12 ± 0.4


HDL-cholesterol (mmol/l)

1.75 ± 0.38

1.32 ± 0.39


LDL-cholesterol (mmol/l)

2.18 ± 0.7

2.09 ± 0.63


Data are given as mean±SD or as counts (%)

Analysing PORH, peak perfusion (mean ± SD) was higher in children with type 1 diabetes than in controls (1.7 ± 0.93 AU vs 1.29 ± 0.46 AU, p = 0.004; Fig. 2a), whereas biological zero was lower in the patient group (0.14 ± 0.04 AU vs 0.19 ± 0.04 AU, p < 0.0001; Fig. 2b). Baseline perfusion (mean ± SD) was similar in both groups (type 1 diabetes 0.45 ± 0.26 AU; controls 0.40 ± 0.16 AU; p = 0.15). In addition, time to peak (type 1 diabetes 14.8 ± 14.1 s; controls 17.6 ± 14.7 s; p = 0.21) and recovery time following peak perfusion (type 1 diabetes 80.3 ± 43.7 s; controls 87.6 ± 38.0 s; p = 0.39) did not differ between groups.
Fig. 2

Peak perfusion (a) and biological zero (b) in children with type 1 diabetes and controls (T1DM n = 58; controls n = 58). Dot plots show the distribution of measured values. A jitter function was used to make all observation points visible. The marked lines indicate the means. T1DM, type 1 diabetes

We found no correlation of HbAlc and the significant microcirculatory parameters (peak perfusion and biological zero). Regarding the duration of disease we found a correlation with peak perfusion (r = 0.287, p = 0.03) but no correlation with biological zero (r = 0.02, p = 0.99).

In a multiple analysis for each microcirculatory parameter we adjusted for all univariately significant risk parameters (BMI, total cholesterol, HDL-cholesterol, blood glucose) as well as for age and sex. Blood pressure z scores did not enter the model since univariately no significance was observed with respect to this parameter. Thus, after adjusting for univariately significant risk parameters, peak perfusion remained higher in children with type 1 diabetes than in controls (p = 0.017) and biological zero remained lower in children with type 1 diabetes than in healthy children (p = 0.009).


We observed differences in microvascular reactivity between children with type 1 diabetes and healthy controls. A main finding was that peak perfusion in superficial microcirculatory networks of the dermis was higher in children with type 1 diabetes than in healthy controls. Since, in the past, various techniques have been used to assess vascular alterations, for interpretation of this result it is important to distinguish between these methods: regarding macrocirculation, it is well known that a sudden release of proximal suprasystolic arterial occlusion causes an immediate increase in blood flow velocity in the large arteries. The corresponding increase in wall shear stress and the subsequent release of nitric oxide conjointly result in a gain of diameter in the large arteries [21].

Over the past two decades flow-mediated dilation of the large arteries has been assessed in numerous studies by duplex ultrasound to detect endothelial dysfunction in the macrocirculation [22]. Furthermore, in recent years peripheral arterial tonometry has evolved to identify endothelial dysfunction by measuring volume changes in the digital arteries using fingertip plethysmography [23, 24, 25]. Peripheral arterial tonometry thereby reflects reactive vasodilation of the peripheral arteries; however, no conclusions can be drawn with respect to microvascular regulation of perfusion in the terminal microcirculatory beds. Most of the cutaneous microcirculation is situated 1–2 mm below the epidermal surface in the papillary dermis and comprises vessels with a diameter below 100 μm [10, 26]. Laser Doppler fluxmetry gathers signals derived from blood cells circulating through this microcirculatory network [10].

Previous studies have shown that thermally and pharmacologically provoked vasodilation—when measured by laser Doppler fluxmetry—is affected in diabetic individuals [27, 28]. While these effects were related to an impaired release of vasoactive agents and reduced vascular susceptibility to these agents, a distinction needs to be drawn with respect to the PORH-induced microvascular response. Employing PORH, the sudden cuff release provokes a complex microvascular response involving myogenic, neurogenic, metabolic and physical factors conjointly aiming at homeostasis of the terminal microcirculatory beds [13]. Homeostasis of capillary perfusion is regulated by the tone of arteriolar smooth muscle cells upstream of the capillary beds. Especially during the early phase of PORH, the rapid increase in blood flow elicits constriction of smooth muscle cells in the wall of precapillary arterioles, which thereby prevents capillary hyperperfusion [12, 13, 29].

Therefore, the higher peak perfusion, which was observed in the microcirculatory beds of children with type 1 diabetes, could be attributed to an impaired regulatory ability of the precapillary arteriolar units.

Several factors could be linked to an altered microvascular autoregulation in children with type 1 diabetes: diabetes mellitus leads to an increase in oxidative stress as well as elevated serum levels of proinflammatory cytokines, which both affect micro- and macrovascular reactivity [30]. Apart from these functional impairments, which primarily involve the vascular endothelium, previous investigations have shown that diabetes mellitus in childhood causes structural changes, such as clumping of elastic fibres and thickening of vascular basement membranes [27, 31]. A thickened arterial wall structure as well as endothelial dysfunction might conjointly cause a reduced ability of microvascular autoregulation. Furthermore, previous studies have shown that autonomic neuropathy potentially occurs at an early stage in individuals with type 1 diabetes [32]. Therefore, affected skin sympathetic activity as a consequence of diabetic autonomic neuropathy might contribute to the hampered regulatory ability within the microcirculatory networks [13, 33].

According to the complexity of the microvascular response, however, it seems reasonable that several pathophysiological mechanisms are responsible for impaired microvascular autoregulation, which might explain the higher peak perfusion in children with type 1 diabetes.

Interestingly, we observed a correlation of peak perfusion with duration of disease in children with type 1 diabetes, while HbAlc levels had no influence on this microcirculatory parameter. In addition, included children with type 1 diabetes were free of clinical manifestations of end-organ damage. This may suggest that apart from glucose metabolism alone, other factors contribute to impaired microvascular autoregulation in type 1 diabetes.

Apart from peak perfusion following PORH, biological zero was lower in children with type 1 diabetes than in healthy controls. Biological zero is the signal that can be detected by laser Doppler fluxmetry when afferent flow is arrested by suprasystolic cuff occlusion [19]. Regarding the origin of biological zero, several physiological sources of this signal have been discussed: apart from collateral circulation through bones, fluid or cellular movement within the interstitial space, movements within vessels unrelated to flow (e.g. red cell sedimentation) and Brownian motion of interstitial macromolecules, residual microvascular vasomotion might contribute to biological zero [19, 34]. However, since the mechanisms causing biological zero are poorly understood, it can only be speculated that the lower biological zero in children with type 1 diabetes might be caused by impaired spontaneous vasomotion.

Notably, vasomotion can be further assessed by spectral analysis of baseline skin perfusion [35]. In former studies spectral analysis revealed impaired neurogenic activity in patients with diabetes mellitus [36]. However, in more recent investigations wavelet analysis, which particularly focused on the neurogenic spectrum of the recorded signal, was unable to identify specific pathophysiological mechanisms in patients with diabetes by means of spectral analysis [37]. Therefore, further investigations are needed to clarify the extent by which several components of spontaneous vasomotion might be affected by diabetes mellitus.

Regarding total resting skin blood flow we compared baseline perfusion between both groups and found no difference between children with type 1 diabetes and healthy controls. Naturally, baseline perfusion can be regarded as a semiquantitative recording comprising total resting skin blood flow. Therefore, from baseline perfusion alone—without PORH—no conclusion can be drawn with respect to the regulation of microvascular blood flow.

Further, recovery time was similar in both groups. During recovery time microcirculation regenerates to its homeostasis. According to the observed difference in peak perfusion one would have expected a prolonged recovery time in children with type 1 diabetes. The missing difference in recovery times between both groups may be explained by different mechanisms that contribute to microcirculatory homeostasis during the initial and late phase of PORH: while the initial phase of PORH is determined by vasoconstrictive ability of smooth muscle cells of precapillary arterioles, recovery time during the late phase of PORH is mainly triggered by mediators of endothelial function such as nitric oxide, prostanoids and adenosine [13, 38, 39, 40, 41]. Therefore, at an early stage type 1 diabetes might influence the vasoconstrictive ability of the precapillary arterioles rather than the release of vasodilatory mediators.

Finally, we observed higher absolute diastolic blood pressure values in healthy controls than in diabetic children. Importantly, blood pressure varies with age, sex and height in childhood. Therefore, blood pressure can be categorised by predefined percentiles and expressed by resulting z scores [42]. To account for age, sex and height dependency of blood pressure we compared z scores but found no significant difference between the groups.

The following limitations of the present study should be acknowledged: previous studies have found that variations in sex hormone levels during puberty or the menstrual cycle might have an impact on vascular reactivity [43, 44]. However, findings on the impact of hormone levels such as serum oestradiol on PORH in microcirculation are inconsistent [45, 46]. Regarding the proportion of female study participants in both groups, a subgroup analysis addressing the stages of the menstrual cycle would have considerably lowered the statistical power of our study. Further, we did not assess pubertal status, since—especially in healthy controls—the respective examinations might have raised severe concerns among volunteering children and parents. Aiming to minimise a possible bias by differences in pubertal development, we therefore strictly compared age- and sex-matched groups. Nevertheless, we cannot exclude a residual confounding by variations in sex hormone levels.

In addition, in controls capillary blood tests were used to determine blood lipid levels, while in children with type 1 diabetes venous blood samples were used. These different techniques had to be applied for logistical reasons. However, the Cholestech LDX System has been proven to have satisfactory accuracy and utility for lipid measurements [47, 48].

In summary, we found that autoregulation of microcirculation of the skin is affected by type 1 diabetes in children. The higher peak perfusion in children with type 1 diabetes might be attributed to a decline in the vasoconstrictive ability of arteriolar smooth muscle cells upstream of capillary beds.


Duality of interest

The Cholestech LDX System was provided by AstraZeneca Austria. The authors declare that there is no further duality of interest associated with this manuscript.

Contribution statement

AWE, GHS and RK were responsible for the conception and design of the study. OS and MEG drafted the manuscript. OS, AH, AWE, MF, BRM, ES, KN, AG, CM, SZ, GHS, RK and MEG analysed and interpreted the data. AH, AWE, MF, BRM, ES, KN, AG, CM, GHS, RK and MEG critically revised the manuscript and contributed to the discussion. SZ was responsible for statistical data analysis and critically revised the manuscript. All authors approved the final version of the manuscript.


  1. 1.
    Patterson CC, Dahlquist GG, Gyurus E, Green A, Soltesz G (2009) Incidence trends for childhood type 1 diabetes in Europe during 1989–2003 and predicted new cases 2005–20: a multicentre prospective registration study. Lancet 373:2027–2033PubMedCrossRefGoogle Scholar
  2. 2.
    Patterson CC, Dahlquist G, Soltesz G, Green A (2001) Is childhood-onset type I diabetes a wealth-related disease? An ecological analysis of European incidence rates. Diabetologia 44(Suppl 3):B9–B16PubMedCrossRefGoogle Scholar
  3. 3.
    McGill HC Jr, McMahan CA, Herderick EE, Malcom GT, Tracy RE, Strong JP (2000) Origin of atherosclerosis in childhood and adolescence. Am J Clin Nutr 72(5 Suppl):1307S–1315SPubMedGoogle Scholar
  4. 4.
    Laing SP, Swerdlow AJ, Slater SD et al (2003) Mortality from heart disease in a cohort of 23,000 patients with insulin-treated diabetes. Diabetologia 46:760–765PubMedCrossRefGoogle Scholar
  5. 5.
    Pambianco G, Costacou T, Ellis D, Becker DJ, Klein R, Orchard TJ (2006) The 30-year natural history of type 1 diabetes complications: the Pittsburgh Epidemiology of Diabetes Complications Study experience. Diabetes 55:1463–1469PubMedCrossRefGoogle Scholar
  6. 6.
    Wardell K, Nilsson GE (1996) Duplex laser Doppler perfusion imaging. Microvasc Res 52:171–182PubMedCrossRefGoogle Scholar
  7. 7.
    Yvonne-Tee GB, Rasool AH, Halim AS, Rahman AR (2005) Reproducibility of different laser Doppler fluximetry parameters of postocclusive reactive hyperemia in human forearm skin. J Pharmacol Toxicol Methods 52:286–292PubMedCrossRefGoogle Scholar
  8. 8.
    Khan F, Green FC, Forsyth JS, Greene SA, Morris AD, Belch JJ (2003) Impaired microvascular function in normal children: effects of adiposity and poor glucose handling. J Physiol 551:705–711PubMedCrossRefGoogle Scholar
  9. 9.
    Humeau A, Saumet JL, L’Huillier JP (2000) Simplified model of laser Doppler signals during reactive hyperaemia. Med Biol Eng Comput 38:80–87PubMedCrossRefGoogle Scholar
  10. 10.
    Braverman IM (1997) The cutaneous microcirculation: ultrastructure and microanatomical organization. Microcirculation 4:329–340PubMedCrossRefGoogle Scholar
  11. 11.
    Braverman IM (2000) The cutaneous microcirculation. J Investig Dermatol Symp Proc 5:3–9PubMedCrossRefGoogle Scholar
  12. 12.
    Yamamoto-Suganuma R, Aso Y (2009) Relationship between post-occlusive forearm skin reactive hyperaemia and vascular disease in patients with Type 2 diabetes—a novel index for detecting micro- and macrovascular dysfunction using laser Doppler flowmetry. Diabet Med 26:83–88PubMedCrossRefGoogle Scholar
  13. 13.
    Strain WD, Chaturvedi N, Bulpitt CJ, Rajkumar C, Shore AC (2005) Albumin excretion rate and cardiovascular risk: could the association be explained by early microvascular dysfunction? Diabetes 54:1816–1822PubMedCrossRefGoogle Scholar
  14. 14.
    Schlager O, Willfort-Ehringer A, Hammer A et al (2011) Microvascular function is impaired in children with morbid obesity. Vasc Med 16:97–102PubMedCrossRefGoogle Scholar
  15. 15.
    Mattock MB, Barnes DJ, Viberti G et al (1998) Microalbuminuria and coronary heart disease in NIDDM: an incidence study. Diabetes 47:1786–1792PubMedCrossRefGoogle Scholar
  16. 16.
    Association AD (2010) Diagnosis and classification of diabetes mellitus. Diabetes Care 33(Suppl 1):S62–S69CrossRefGoogle Scholar
  17. 17.
    American Academy of Pediatrics–Committee on Nutrition (1998) Cholesterol in childhood. Pediatrics 101:141–147Google Scholar
  18. 18.
    Wardell K, Jakobsson A, Nilsson GE (1993) Laser Doppler perfusion imaging by dynamic light scattering. IEEE Trans Biomed Eng 40:309–316PubMedCrossRefGoogle Scholar
  19. 19.
    Kernick DP, Tooke JE, Shore AC (1999) The biological zero signal in laser Doppler fluximetry—origins and practical implications. Pflugers Arch 437:624–631PubMedCrossRefGoogle Scholar
  20. 20.
    Roustit M, Cracowski JL (2012) Non-invasive assessment of skin microvascular function in humans: an insight into methods. Microcirculation 19:47–64Google Scholar
  21. 21.
    Moens AL, Goovaerts I, Claeys MJ, Vrints CJ (2005) Flow-mediated vasodilation: a diagnostic instrument, or an experimental tool? Chest 127:2254–2263PubMedCrossRefGoogle Scholar
  22. 22.
    Corretti MC, Anderson TJ, Benjamin EJ et al (2002) Guidelines for the ultrasound assessment of endothelial-dependent flow-mediated vasodilation of the brachial artery: a report of the International Brachial Artery Reactivity Task Force. J Am Coll Cardiol 39:257–265PubMedCrossRefGoogle Scholar
  23. 23.
    Rubinshtein R, Kuvin JT, Soffler M et al (2010) Assessment of endothelial function by non-invasive peripheral arterial tonometry predicts late cardiovascular adverse events. Eur Heart J 31:1142–1148PubMedCrossRefGoogle Scholar
  24. 24.
    Haller MJ, Stein J, Shuster J et al (2007) Peripheral artery tonometry demonstrates altered endothelial function in children with type 1 diabetes. Pediatr Diabetes 8:193–198PubMedCrossRefGoogle Scholar
  25. 25.
    Mahmud FH, Earing MG, Lee RA, Lteif AN, Driscoll DJ, Lerman A (2006) Altered endothelial function in asymptomatic male adolescents with type 1 diabetes. Congenit Heart Dis 1:98–103PubMedCrossRefGoogle Scholar
  26. 26.
    Reid L, Meyrick B (1982) Microcirculation: definition and organization at tissue level. Ann N Y Acad Sci 384:3–20PubMedCrossRefGoogle Scholar
  27. 27.
    Shore AC, Price KJ, Sandeman DD, Green EM, Tripp JH, Tooke JE (1991) Impaired microvascular hyperaemic response in children with diabetes mellitus. Diabet Med 8:619–623PubMedCrossRefGoogle Scholar
  28. 28.
    Khan F, Elhadd TA, Greene SA, Belch JJ (2000) Impaired skin microvascular function in children, adolescents, and young adults with type 1 diabetes. Diabetes Care 23:215–220PubMedCrossRefGoogle Scholar
  29. 29.
    Matchkov VV, Tarasova OS, Mulvany MJ, Nilsson H (2002) Myogenic response of rat femoral small arteries in relation to wall structure and [Ca(2+)](i). Am J Physiol Heart Circ Physiol 283:H118–H125PubMedGoogle Scholar
  30. 30.
    Caballero AE, Arora S, Saouaf R et al (1999) Microvascular and macrovascular reactivity is reduced in subjects at risk for type 2 diabetes. Diabetes 48:1856–1862PubMedCrossRefGoogle Scholar
  31. 31.
    Cox NH, McCruden D, McQueen A et al (1987) Histological findings in clinically normal skin of patients with insulin-dependent diabetes. Clin Exp Dermatol 12:250–255PubMedCrossRefGoogle Scholar
  32. 32.
    Pfeifer MA, Weinberg CR, Cook DL et al (1984) Autonomic neural dysfunction in recently diagnosed diabetic subjects. Diabetes Care 7:447–453PubMedCrossRefGoogle Scholar
  33. 33.
    Vinik AI, Maser RE, Mitchell BD, Freeman R (2003) Diabetic autonomic neuropathy. Diabetes Care 26:1553–1579PubMedCrossRefGoogle Scholar
  34. 34.
    Strain WD, Chaturvedi N, Nihoyannopoulos P, Bulpitt CJ, Rajkumar C, Shore AC (2005) Differences in the association between type 2 diabetes and impaired microvascular function among Europeans and African Caribbeans. Diabetologia 48:2269–2277PubMedCrossRefGoogle Scholar
  35. 35.
    Rossi M, Bertuglia S, Varanini M, Giusti A, Santoro G, Carpi A (2005) Generalised wavelet analysis of cutaneous flowmotion during post-occlusive reactive hyperaemia in patients with peripheral arterial obstructive disease. Biomed Pharmacother 59:233–239PubMedCrossRefGoogle Scholar
  36. 36.
    Rossi M, Ricordi L, Mevio E et al (1990) Autonomic nervous system and microcirculation in diabetes. J Auton Nerv Syst 30(Suppl):S133–S135PubMedCrossRefGoogle Scholar
  37. 37.
    Urbancic-Rovan V, Stefanovska A, Bernjak A, Azman-Juvan K, Kocijancic A (2004) Skin blood flow in the upper and lower extremities of diabetic patients with and without autonomic neuropathy. J Vasc Res 41:535–545PubMedCrossRefGoogle Scholar
  38. 38.
    Wong BJ, Wilkins BW, Holowatz LA, Minson CT (2003) Nitric oxide synthase inhibition does not alter the reactive hyperemic response in the cutaneous circulation. J Appl Physiol 95:504–510PubMedGoogle Scholar
  39. 39.
    Zhao JL, Pergola PE, Roman LJ, Kellogg DL Jr (2004) Bioactive nitric oxide concentration does not increase during reactive hyperemia in human skin. J Appl Physiol 96:628–632PubMedCrossRefGoogle Scholar
  40. 40.
    Binggeli C, Spieker LE, Corti R et al (2003) Statins enhance postischemic hyperemia in the skin circulation of hypercholesterolemic patients: a monitoring test of endothelial dysfunction for clinical practice? J Am Coll Cardiol 42:71–77PubMedCrossRefGoogle Scholar
  41. 41.
    Larkin SW, Williams TJ (1993) Evidence for sensory nerve involvement in cutaneous reactive hyperemia in humans. Circ Res 73:147–154PubMedGoogle Scholar
  42. 42.
    National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents (2004) The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics 114:555–576CrossRefGoogle Scholar
  43. 43.
    Elhadd TA, Khan F, Kirk G et al (1998) Influence of puberty on endothelial dysfunction and oxidative stress in young patients with type 1 diabetes. Diabetes Care 21:1990–1996PubMedCrossRefGoogle Scholar
  44. 44.
    Adkisson EJ, Casey DP, Beck DT, Gurovich AN, Martin JS, Braith RW (2010) Central, peripheral and resistance arterial reactivity: fluctuates during the phases of the menstrual cycle. Exp Biol Med (Maywood) 235:111–118CrossRefGoogle Scholar
  45. 45.
    Gerhardt U, Hillebrand U, Mehrens T, Hohage H (2000) Impact of estradiol blood concentrations on skin capillary Laser Doppler flow in premenopausal women. Int J Cardiol 75:59–64PubMedCrossRefGoogle Scholar
  46. 46.
    Clifton VL, Crompton R, Read MA, Gibson PG, Smith R, Wright IM (2005) Microvascular effects of corticotropin-releasing hormone in human skin vary in relation to estrogen concentration during the menstrual cycle. J Endocrinol 186:69–76PubMedCrossRefGoogle Scholar
  47. 47.
    Carey M, Markham C, Gaffney P, Boran C, Maher V (2006) Validation of a point of care lipid analyser using a hospital based reference laboratory. Ir J Med Sci 175:30–35PubMedCrossRefGoogle Scholar
  48. 48.
    Parikh P, Mochari H, Mosca L (2009) Clinical utility of a fingerstick technology to identify individuals with abnormal blood lipids and high-sensitivity C-reactive protein levels. Am J Health Promot 23:279–282PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • O. Schlager
    • 1
  • A. Hammer
    • 1
  • A. Willfort-Ehringer
    • 1
  • M. Fritsch
    • 2
  • B. Rami-Merhar
    • 2
  • E. Schober
    • 2
  • K. Nagl
    • 1
  • A. Giurgea
    • 1
  • C. Margeta
    • 1
  • S. Zehetmayer
    • 3
  • G. H. Schernthaner
    • 1
  • R. Koppensteiner
    • 1
  • M. E. Gschwandtner
    • 1
    Email author
  1. 1.Department of Medicine II, Division of AngiologyMedical University of ViennaViennaAustria
  2. 2.Department of Pediatrics and Adolescent MedicineMedical University of ViennaViennaAustria
  3. 3.Center for Medical Statistics, Informatics and Intelligent SystemsMedical University of ViennaViennaAustria

Personalised recommendations